FPGA-driven pseudorandom number generators aimed at accelerating Monte Carlo methods
Bibliographic record
Abstract
Hardware acceleration in High Performance Computing (HPC) context is of growing interest, particularly in the field of Monte Carlo methods where the resort to Field Programmable Gate Array (FPGA) technology has been proven as an effective media, capable of enhancing by several orders the speed execution of stochastic processes. The spread-use of reconfigurable hardware for stochastic simulation gathered a significant effort towards effective implementations of hardware pseudorandom numbers generators (PRNGs) - these generators needed to exhibit a statistically proven random behaviour and to be charactarized by a very long period. In this paper we present the state of the art of hardware pseudorandom number generation in the context of Monte Carlo acceleration. We highlight the emerging trends over the most recent publications and suggest some insights on the forthcoming works. Furthermore, we provide a complete hardware description of a new gaussian variate generator (GVG) and an exponential variate generator (EVG) based on a decision-tree technique of ours, herein presented as well. The prototypes implemented on a Xilinx Virtex II Pro XC2VP100 FPGA occupy from 150 to 417 slices and reach 280 MHz, while exhibiting good statistical behaviours with high p-values on the x2test and offering a unitary Knuth ratio.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".